Methods and apparatus for material evaluation using laser speckle
Methods for measuring strains in biological and other samples include illuminating a specimen with substantially collimated laser flux. An electro-acoustic transducer is activated to generate acoustic waves in the specimen, including a surface wave component such as a Rayleigh wave. A series of laser speckle patterns produced by a laser flux scattered or reflected by the specimen is recorded and speckle pattern shifts are calculated based on the recorded speckle patterns. Phase shifts produced by acoustic wave propagation in the specimen are used to identify specimen regions associated with irregularities such as inclusions, cracks, or tissue abnormalities. In some examples, specimens are stretched or otherwise stressed by one or more light fluxes, and specimen elongation is estimated based on a series of associated speckle patterns.
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This application claims priority from Provisional Patent Application 60/370,927, filed Apr. 8, 2002.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCHThe U.S. government has a paid-up license in this invention and the right in limited circumstances to require the patent owner to license to others on reasonable terms as provided for by the terms of Contract BES-0086719 awarded by the National Science Foundation.
TECHNICAL FIELDThe invention pertains to laser speckle based materials evaluation.
BACKGROUNDOptical non-destructive evaluation (NDE) has been used in numerous materials characterization applications in the aerospace, semiconductor, and other industries. Conventional optical NDE methods are based on speckle interferometry, speckle photography, or double-exposure holography. Speckle-based methods take advantage of the speckle patterns produced by the scattering of monochromatic light, such as a laser light flux, from a rough surface. As a result, laser based optical speckle methods do not require specimen contact to assess specimen properties.
Speckle-based methods tend to be unsuited to measurement of many specimens of interest, frequently because of difficulties associated with either the application of a test stimulus or interrogation of the response to the stimulus. In addition, conventional methods frequently do not provide sufficient measurement resolution. While some optical NDE methods have been used to evaluate biological specimens in medical research applications, these methods are generally unsuitable for clinical applications. For example, clinical strain measurements of biological tissues or cell distortions are especially difficult. Accordingly, improved measurement methods and apparatus are needed.
SUMMARYMeasurement systems for assessing elastic properties of a specimen include an electro-acoustic transducer configured to produce an acoustic wave in the specimen, and a light source configured to deliver a substantially collimated light flux to the specimen to produce a speckle pattern. An image capture device is situated to record a series of speckle patterns and a signal processor receives a series of such recorded speckle patterns and determines a speckle shift associated with the acoustic wave. According to representative examples, the systems include a memory configured to store the series of recorded speckle patterns and a lens configured so that the speckle patterns recorded by the image capture device are imaged speckle patterns. In additional examples, the image capture device records a series of two dimensional speckle patterns and two dimensional speckle shifts are determined for one or more specimen locations. According to other examples, the electro-acoustic transducer is situated to produce a surface acoustic wave such as a Rayleigh wave. In other examples, the signal processor is configured to determine speckle shifts based on a maximum entropy method, a minimum mean squares method, or other method.
Measurement apparatus include at least one light source configured to alternately illuminate a specimen at a first angle of incidence and a second angle of incidence, and produce corresponding first series and second series of speckle patterns. An image capture device is configured to capture the first and second series of speckle patterns. A signal processor determines first and second speckle shifts based on the first and second series of captured speckle patterns. According to representative examples, the first angle of incidence and the second angle of incidence are approximately opposite and equal in magnitude. In representative examples, a two-dimensional strain map is produced based on the speckle shifts as a function of specimen location.
Apparatus for assessing a tissue specimen include a light source configured to illuminate the tissue specimen and produce speckle patterns, and an image capture device configured to record the speckle patterns. An electro-acoustic transducer is configured to produce an acoustic wave and a speckle record processor estimates a speckle shift produced by the acoustic wave. In some examples, the apparatus include an indicator configured to provide an assessment metric based on the speckle shift. In still further examples, the speckle record processor is configured to estimate speckle shifts associated with at least two regions of the tissue specimen, and the assessment metric is based on a comparison of the speckle shifts.
Methods of producing temporally varying speckle patterns include illuminating a specimen with a light flux and generating an acoustic wave in the specimen at an acoustic frequency having an associated acoustic wavelength that is substantially larger than the illuminated region of the specimen. According to representative examples, the acoustic wave is a surface acoustic wave.
Methods of assessing a tissue sample include measuring a first speckle shift associated with a first region of the tissue sample and a second speckle shift associated with a second region of the tissue sample. The first speckle shift and the second speckle shift are compared and an abnormality is indicated based on the comparison. In other examples, the methods include producing speckle patterns associated with the first speckle shifts and the second speckle shifts by propagating an acoustic wave at a frequency of less than about 10 Hz in the tissue sample.
Apparatus for determining specimen distortion include a laser configured to provide component beams directed to the specimen so that the specimen is distorted. A light illuminates the distorted specimen and produces a speckle pattern based on the specimen. An imaging device is configured to record a series of speckle pattern images and a processor provides an estimate of specimen distortion based on the series of speckle patterns. According to representative embodiments, the specimen is a cell and a flow chamber is provided to retain the cell in a fluid flow.
Methods of determining a distortion of a specimen include distorting the specimen with at least one light flux and obtaining a speckle pattern image series based on a speckle pattern produced by the specimen. The specimen distortion is estimated based on the speckle pattern image series. According to representative embodiments, the specimen is distorted using two counter-propagating laser beams. In additional examples, the specimen is trapped using the at least one light flux.
These and other features are set forth below with reference to the accompanying drawings.
With reference to
An electro-acoustic transducer 108 or other acoustic transducer is situated on or near a surface 105 of the specimen 106, and sufficiently close to or in contact with the specimen 106 so that acoustic waves can be generated in at least the region of interest 104. A small speaker can serve as the electro-acoustic transducer 108. A function generator 110 or other electrical signal generator that produces electrical signals suitable for stimulating acoustic waves is electrically connected to the electro-acoustic transducer 108. The function generator typically produces electrical signals of amplitudes of between about 0.1 V and 25 V and at frequencies between about 0.1 Hz and 10 Hz, or at infrasound frequencies between about 1 Hz and 3 Hz. The function generator 110 can be configured to provide various electrical waveforms such as sinusoidal waveforms, triangular waveforms, and square waveforms. Such electrical signals applied to the electro-acoustic transducer 110 tend to produce acoustic waves that propagate in the specimen 106 as surface waves such as Rayleigh waves. Propagation of such surface waves depends on the stiffness of subsurface materials so that subsurface features of the specimen 106 contribute to acoustic propagation characteristics.
A telecentric lens 112 or other lens is situated to receive portions of the light flux reflected or scattered at the region of interest 104 and deliver the received portions to an optical sensor array 114. The sensor array 114 can be a one dimensional array suitable for producing line images or a two dimensional array that can produce two dimensional images. Accordingly, the sensor array 114 provides electrical signals associated with one or two dimensional speckle pattern records (images) to a signal processor 116. Before describing operation of the apparatus 100, some properties of speckle patterns are briefly described.
Speckle PropertiesSpeckle patterns produced by hydrated tissues (i.e., biological specimens) tend to exhibit decorrelation and are typically at least partially depolarized relative to an incident light flux. Hydrated tissues are generally considered to be adequately described as randomly inhomogeneous media, and the randomness of the tissue structure results in multiple scattering events leading to depolarization. Some tissues are exceptions such as, for example, certain transparent tissues such as eye tissues, mucous membranes, and superficial skin layers. Hydrated tissues tend to swell and shrink, and therefore speckle patterns associated with a particular region of interest tend to change in appearance or decorrelate.
Speckle-based measurements can use first order speckle statistics, second order speckle statistics, or other speckle properties. First order statistics relate to speckle pattern properties at a single point, while second order statistics relate to joint statistical properties of a speckle pattern at two or more points, and can be associated with speckle granularity and speckle size. Mean speckle size in a speckle field depends upon viewing system aperture. For so-called objective (non-imaged) speckle, an effective aperture is a diameter D of a uniformly illuminated area on a scattering surface. Mean speckle diameter dsp at a screen a distance z from a scattering plane is approximately
dsp≈1.2λoz/D,
wherein λo is an illumination wavelength. If a lens is used to image a speckle pattern (imaged or so-called subjective speckle), then mean speckle size dsp is approximately
dsp≈2.42λov/2Ra,
where v is a distance from the lens to the imaging plane and 2Ra is a diameter of the viewing lens aperture. If a magnification M is provided, then
dsp≈1.2(1+M)λov/2Ra.
The above approximations are based on an assumption that the lens images the scattering surface, but such imaging is not required.
In some representative examples, test systems are configured to assess specific specimen responses to an acoustic stimulus. Yamaguchi, “Simplified laser speckle strain gauge,” Opt. Eng 21:436–440 (1982), discloses that for an object undergoing strain, speckle shift observed at an angle θo for an illumination angle θs is given by:
wherein ax is an in-plane motion, az is an out-of-plane motion, εxx is a linear strain in a plane of the detector and laser beams (typically the parameter of interest), Ωy is a rotation about an axis perpendicular to the measurement plane, Ls is a radius of an illuminating wavefront (i.e., source distance), and Lo is an observation distance. Using a configuration such as that of
δA≡δx(0,+θs)−δx(0,−θs)=−2Loεxx sin θs.
Thus, the in-plane strain term can be isolated. Using a complementary configuration with two cameras and a single illumination angle,
δA≡δx(+θo,0)−δx(−θo,0)=−2Loεxx tan θo−2az sin θo
and the desired strain term can be isolated if a term containing az is negligible with an appropriate choice of Lo and θo.
Referring again to
Speckle records are captured sequentially. For a two dimensional image sensor, speckle amplitudes gij of a pixel in an ith row and a jth column are recorded, and the recording process continues until a total Nrec of speckle records are captured. A representative two dimensional stacked speckle history 200 is illustrated in
In an example, the apparatus of
The porcine skin specimen was injected with 1 mL of 25% glutaraldehyde to produce artificial lesions of about 1.5 cm diameter at a location 118 between the electro-acoustic transducer 108 and the ROI 104. Loss tangents for untreated and treated porcine skin were obtained in three measurements using ten stacked speckle histories in each measurement. The mechanical loss factor tan δ for porcine skin decreases significantly with the addition of glutaraldehyde, indicating a reduced contribution by a viscous component of the specimen, reducing energy loss. The table below includes mean values of tan δ for three sets of ten measurements of untreated and glutaraldehyde-treated porcine skin.
While example measurements of a porcine skin specimen are described, clinical measurements can be performed to, for example, assess swelling or identify or characterize tumors or skin lesions, or other specimens can be evaluated. In addition, an unilluminated specimen region can be assessed by directing an acoustic signal through the region and estimating, for example, an acoustic phase shift associated with the region.
Two-dimensional maps of elastic and viscoelastic properties of specimens such as industrial materials, biomaterials, biological tissues, cells, and engineered tissues can be obtained using an optical measurement apparatus 500 illustrated in
The light fluxes 511, 515 are thus delivered to a region of interest 520. A lens 522 and a camera 524 receive portions of the light fluxes 511, 515 reflected or scattered by the specimen 516 and deliver electrical signals associated with speckle records to a signal processor 526. An electro-acoustic transducer 530 or other acoustic transducer is situated on or near a surface 535 of the specimen 516, and sufficiently close or in contact with the specimen 516 so that acoustic waves can be generated in at least the region of interest 520. A function generator 540 or other electrical signal generator produces electrical signals suitable for producing acoustic waves and is electrically connected to the electro-acoustic transducer 530.
Alternative methods and apparatus can be used to stress samples. For example, specimens can be stressed in a tensile testing machine. Biological cells or other specimens can be stretched using a laser-based cell stretcher as described in, for example, Guck et al., Biophysical Journal 81:767–784 (2001) and Kas et al., U.S. Pat. No. 6,067,859. Referring to
The component beams 558, 560 are configured to stretch the cell 570 or other specimen based on applied laser power and pulse repetition rate. The component beams 558, 560 can also be configured to retain or trap the cell 570 in the aperture. Cell deformation or stretching can be evaluated based on speckle pattern shifts as described above with reference to
The component beams 558, 560 can be conveniently delivered using single mode or multimode optical fibers. Component beam optical power can be modulated using an electro-optic modulator, an acousto-optic modulator, or other optical modulator. Optical power and beam wavelength can be selected so that distortions of living cells can be estimated.
The apparatus 500 of
Speckle shifts can be estimated using several methods. For convenience, these methods can be classified as non-parametric methods or parametric methods. Non-parametric methods generally are not based on assumptions about measured speckle shifts. Representative examples of a non-parametric methods are methods based on cross-correlations of sequential speckle records, in which shifts of correlation peaks between the speckle records are associated with speckle pattern shifts. To improve computational efficiency, cross-correlations are typically calculated using Fourier transforms. Parametric methods include minimum mean square error methods, maximum likelihood methods, and maximum entropy methods. These parametric methods are described below. For convenience in describing these methods, it is assumed that over a time period in which at least a few sequential speckle records are obtained, the structure of the speckle pattern is fixed and the only change with time is a lateral translation of the speckle pattern. In addition, the methods are described below with reference to one dimensional speckle records or two dimensional speckle records.
Minimum Mean Square Error MethodsSpeckle motion can be modeled as:
gj+1(xi)=gj(xi−δx), (1)
wherein g is a speckle record pixel value, δx is speckle shift, i denotes an ith pixel in a speckle record, and j represents a speckle record number. For two dimensional speckle records, an additional index can be used. If the speckle shift δx is small compared to pixel size, Eq. (1) can be approximated as
gj+1(xi)≈gj(xi)+δxg′j(xi), (2)
corresponding to two terms of a Taylor series expansion for g. For computational convenience, speckle records on either side of a record of interest are selected for evaluation so that terms
[gj+1(xi), gj−1(xi)], (3)
are selected. The speckle shift δx is calculated to minimize a mean square error
wherein the summation is performed over all pixels in the speckle records. Eq. (4) can be solved using a gradient search algorithm. Alternatively, using the small speckle motion approximation of Eq. (2), differentiation with respect to δx and rearrangement produces the following relationship:
The term in the first square bracket in the numerator is a first central difference approximation to a derivative,
Spatial derivatives may be approximated as
The speckle shift δx is a time rate at which a speckle pattern shifts in units of pixels/record.
Although the above example method is based on speckle records on either side of a selected record, additional records can be included and their contributions weighted appropriately to estimate higher order approximations. For example, instead of using the weights
that correspond to records on either side of a selected record, the weights
can be used so that two records on either side of the selected record are used. With such weights, the mean square error is:
the temporal derivative term of Eq. (5) is
and the term involving spatial derivatives is
[−2g′j−2(xi)+8g′j−1(xi)+8g′j+1(xi)−2g′j+2(xi)]. (12)
Note that this is simply a weighted average,
of terms on either side of the selected record. These higher order operators for the derivative can be used to estimate the spatial derivatives:
The higher order approximations to the derivative typically exhibit superior noise rejection but at the expense of reduced temporal resolution.
The only a priori assumption used in the above methods is that the speckle shift is small with respect to the pixel size. If it is further assumed that measured speckle records include a deterministic speckle signal component and a noise component, the measured signal can be modeled as
dj(xi)=gj(xi)+nj(xi), (15)
wherein n is a zero-mean noise signal. If the central difference about the jth record is calculated,
dj+1(xi+δx)−dj−1(xi−δx)=gj+1(xi+δx)−gj−1(xi−δx)+nj+1(xi+δx)−nj−1(xi−δx). (16)
Sequential speckle patterns are assumed to have a constant mean and the noise is a assumed to be a statistically independent Gaussian random variable with zero mean and constant variance. As a result the noise probability density function can be written as
wherein C is a constant. Equation (17) is referred to as a likelihood function. To choose the δx that maximizes this likelihood, the following equation is solved:
Carrying out this calculation leads to the formula in Eq. (5). It can be shown that this provides an unbiased estimate of the speckle pattern shift and that the variance of the estimate attains the Cramér-Rao lower bound.
Additional methods are based on maximum entropy estimators. Speckle motion is modeled as
gj+1(xi)=gj(xi−δx), (19)
wherein the index j denotes a speckle record number and the index i denotes a selected pixel in the speckle record. According to Eq. (19), the difference between speckle records j and j+1 is that the latter is shifted by δx. A shift parameter can be estimated by shifting a pair of records in opposite directions and forming a quotient R of the shifted records, i.e.
An entropy parameter H is then calculated as follows:
wherein
Using the normalization of Eq. (22),
By maximizing the entropy parameter H of Eq. (21), a shift parameter can be selected that most nearly shifts the numerator and denominator of Eq. (20) into registration to produce a maximally flat quotient. If the distribution p is perfectly flat, the associated entropy is simply log(N). A gradient search algorithm can be used to estimate the shift parameter.
Speckle pattern records generally are interpolated to determine shifted speckle patterns. This interpolation is conveniently performed using the Fourier shift theorem:
gj(xi−δx)=F−1{e−j2πδxf
wherein
Gj(fk)=F{gj(xi)}, (25)
and F and F−1 refer to Fourier transforms and inverse Fourier transforms, respectively. The ratio of Eq. (20) can be obtained as
Such methods determine values of δx that make this ratio the “flattest.” (Other methods such as those based on Eq. 4 can also be made more efficient using the Fourier shift theorem and a gradient search algorithm without assuming that speckle shifts are small.) As implemented, the innermost transforms are computed only once, so that at each iteration of a gradient search, only the following is computed:
Since the model of Eq. (19) is only approximate, there are occasional noisy spikes in the ratio calculated as in Eq. (20). To avoid the possibility of the gradient search algorithm being trapped in a local maximum, a median filter is applied to the ratio prior to normalization and computation of entropy so that an entropy surface is convex. The gradient algorithm can also be modified to use any a priori knowledge of the amount of speckle record shift. For example, the gradient search can be restricted to a limited range of values. Alternatively, methods can be based on obtaining a minimum value of negative entropy.
With reference to
wherein (δx,δy) are x- and y-displacements, gk−1, g, gk+1 are pixel values associated with the speckle images 602k−1, 602k, 602k+1, respectively, and xi, yj, are pixel spatial coordinates. The summations are generally performed over, for example, a small subset of pixel values, typically pixel values associated with an array of about 3 pixels by 3 pixels as illustrated in
Displacements (δx,δy) are estimated such that features in the selected speckle images are brought approximately into registration. Pixel values can be represented as follows using a two-dimensional Taylor series expansion:
Assuming that feature shifts are small, the approximations
can be used, and the expression for the mean square error becomes:
For convenience, the following definitions are used:
Using these definitions, the equations
produce a set of simultaneous equations:
This set of equations can be inverted to obtain the displacement estimates (δx,δy):
The arrays of Equations 32c–32e are N×M arrays and displacement estimates can be produced for every pixel or selected pixels.
Spatial gradients can be calculated based on, for example, a central difference formula so that
The indicated summations, for each sub-element of the image pairs, can be calculated using convolution operations. For example, a (1,1) element of the system matrix in Eq. 32c can be as:
In this equation, the squaring operation is performed element-by element. The result of the convolution operations is a matrix that grows in height by three elements and width by six elements. The additional pixels can be discarded. Similarly,
In Equations 34–35, the first convolution is with a derivative operator and the second convolution performs a local sum. In the example of Equations 34–35, derivatives are obtained based on a central difference estimate, but in additional examples, optimized derivative filters or other methods can be used. The operations of Equations 34–35 can extend matrix height and width by three rows and three columns, but these additional values can be discarded.
Methods and apparatus for quantitative imaging of elastic and viscoelastic properties of materials such as human tissues and cells based on stacked speckle histories can provide surface maps of simple and complex plane strains. Two-dimensional maps of speckle shifts quantitatively describe, for example, an in-plane strain field in a test specimen. Regions with large or small strains can be imaged and mapped to identify, for example, inhomogeneities such as a subsurface defect, or a subdermal lesion, other pathological tissue, or other specimen feature.
As shown above, by applying a selected stress (acoustic wave) to a specimen, spatially resolved elastic constants of the specimen such as, for example, Young's modulus, can be obtained. Based on a phase of an applied stress, a mechanical loss factor (tan δ) and other derived viscoelastic properties can be evaluated. Two-dimensional strain fields can be image and evaluated using apparatus such as shown in
Examples are described above but can be changed in arrangement and detail without departing from the claimed subject matter. For example, illumination sources of different wavelengths can be used, and one, two, or more illumination sources can be provided in a single apparatus. Illumination can be provided to specimens at two equal and opposite angles or at two different angles. Acoustic frequency can be varied and speckle record acquisition rates can be increased or decreased. We claim all that is encompassed by the appended claims.
Claims
1. An apparatus for assessing elastic properties of a specimen, comprising: a light source configured to deliver a light flux to the specimen and produce an optical speckle pattern that is recorded by an image capture device; and
- an electro-acoustic transducer configured to produce an acoustic wave in the specimen;
- a signal processor configured to receive a series of recorded optical speckle patterns and determine an optical speckle shift associated with the acoustic wave based on estimating shifted values of portions of at least two recorded optical speckle patterns.
2. The apparatus of claim 1, further comprising a memory configured to store the series of recorded optical speckle patterns.
3. The apparatus of claim 1, further comprising a lens configured so that the optical speckle pattern recorded by the image capture device is an imaged optical speckle pattern.
4. The apparatus of claim 1, wherein the image capture device records two-dimensional optical speckle patterns.
5. The apparatus of claim 4, wherein the signal processor is configured to determine optical speckle shifts associated with a plurality of specimen locations.
6. The apparatus of claim 1, wherein the acoustic wave produced by the electro-acoustic transducer includes a surface acoustic wave.
7. The apparatus of claim 1, wherein the signal processor is configured to determine the optical speckle shift based on a sum of a ratio of corresponding estimated shifted values of the portions of the at least two recorded optical speckle patterns.
8. The apparatus of claim 1, wherein the signal processor is configured to determine the optical speckle shift based substantially minimizing a sum of a square of differences between corresponding estimated shifted values of the portions of the at least two recorded optical speckle patterns.
9. The apparatus of claim 1, wherein the signal processor estimates the shifted values of the portions of the at least two recorded optical speckle patterns based on products of trial values of the estimated speckle shift and estimated values of derivatives of recorded values of the optical speckle patterns.
10. An apparatus, comprising:
- at least one light source configured to alternately illuminate a specimen at a first angle of incidence and a second angle of incidence, and produce a first series of optical speckle patterns and a second series of optical speckle patterns;
- an image capture device configured to capture the first and second series of optical speckle patterns; and
- a signal processor configured to determine first and second speckle shifts based on the first and second series of recorded optical speckle patterns based on interpolated speckle shifts obtained from the recorded optical speckle patterns.
11. The apparatus of claim 10, wherein the first angle of incidence and the second angle of incidence are approximately equal and opposite.
12. An apparatus for assessing a tissue specimen, comprising: an electro-acoustic transducer configured to produce an acoustic wave;
- a light source configured to illuminate the tissue specimen and produce optical speckle patterns;
- an image capture device configured to record the optical speckle patterns;
- a speckle record processor configured to determine estimated shifted speckle patterns for at least portions of the recorded optical speckle patterns, and to estimate speckle shifts in the optical speckle patterns produced by the acoustic wave based on the estimated shifted speckle patterns.
13. The apparatus of claim 12, further comprising an indicator configured to provide an assessment metric based on the speckle shift.
14. The apparatus of claim 13, wherein the speckle record processor is configured to estimate speckle shifts associated with at least two regions of the tissue specimen and the assessment metric is based on a comparison of the speckle shifts.
15. The apparatus of claim 12, wherein the speckle record processor is configured to estimate speckle shifts associated with at least two regions of the tissue specimen.
16. A method of assessing a tissue sample, comprising:
- measuring a first speckle shift associated with a first region of the tissue sample and a second speckle shift associated with a second region of the tissue sample; and
- comparing the first speckle shift and the second speckle shift.
17. The method of claim 16, further comprising indicating an abnormality based on the comparison.
18. The method of claim 16, further comprising producing speckle patterns associated with the first speckle shift and the second speckle shift by propagating an acoustic wave in the tissue sample.
19. The method of claim 18, wherein the acoustic wave has a frequency of less than about 10 Hz.
20. The method of claim 19, wherein the acoustic wave is a surface wave.
21. An apparatus for determining specimen distortion, comprising:
- a laser configured to provide component beams directed to the specimen so that the specimen is distorted;
- a light source configured to illuminate the distorted specimen and produce a speckle pattern;
- an image device configured to record a series of speckle pattern images; and
- a processor configured to determine estimated speckle pattern images and provide an estimate of specimen distortion based on the series of estimated speckle pattern images.
22. The apparatus of claim 21, wherein the specimen is a cell.
23. The apparatus of claim 21, further comprising a flow chamber configured to provide the specimen in a fluid flow.
24. A method of determining a distortion of a specimen, comprising:
- distorting the specimen with at least one light flux;
- obtaining a speckle pattern image series based on a speckle pattern produced by the specimen, and based on the speckle pattern image series, produce estimated speckle pattern images; and
- estimating a specimen distortion based on the estimated speckle pattern images.
25. The method of claim 24, wherein the specimen is distorted using two counter-propagating laser beams.
26. The method of claim 24, wherein the specimen is trapped with the light flux.
27. An apparatus for assessing elastic properties of a specimen, comprising: a light source configured to deliver a light flux to the specimen and produce an optical speckle pattern that is recorded by an image capture device; and
- an electro-acoustic transducer configured to produce an acoustic wave in the specimen;
- a signal processor configured to receive a series of recorded optical speckle patterns and determine an optical speckle shift associated with the acoustic wave, wherein the signal processor is configured to determine optical speckle shifts associated with a plurality of specimen locations.
28. The apparatus of claim 27, further comprising a memory configured to store the series of recorded optical speckle patterns.
29. The apparatus of claim 27, further comprising a lens configured so that the optical speckle pattern recorded by the image capture device is an imaged optical speckle pattern.
30. The apparatus of claim 27, wherein the image capture device records two-dimensional optical speckle patterns.
31. The apparatus of claim 27, wherein the acoustic wave produced by the electro-acoustic transducer includes a surface acoustic wave.
32. The apparatus of claim 27, wherein the signal processor is configured to determine the optical speckle shift based on a maximum entropy method.
33. The apparatus of claim 27, wherein the signal processor is configured to determine the optical speckle shift based on a minimum mean square method.
34. An apparatus for assessing a tissue specimen, comprising: an electro-acoustic transducer configured to produce an acoustic wave; an indicator configured to provide an assessment metric based on the speckle shift, wherein the speckle record processor is configured to estimate speckle shifts associated with at least two regions of the tissue specimen and the assessment metric is based on a comparison of the speckle shifts.
- a light source configured to illuminate the tissue specimen and produce optical speckle patterns;
- an image capture device configured to record the optical speckle patterns;
- a speckle record processor configured to estimate speckle shifts in the optical speckle patterns produced by the acoustic wave; and
35. An apparatus for assessing a tissue specimen, comprising: an electro-acoustic transducer configured to produce an acoustic wave;
- a light source configured to illuminate the tissue specimen and produce optical speckle patterns;
- an image capture device configured to record the optical speckle patterns;
- a speckle record processor configured to estimate speckle shifts in the optical speckle patterns produced by the acoustic wave, wherein the speckle record processor is configured to estimate speckle shifts associated with at least two regions of the tissue specimen.
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Type: Grant
Filed: Jun 14, 2002
Date of Patent: Aug 8, 2006
Assignee: Providence Health Systems —Oregon (Portland, OR)
Inventors: Sean J. Kirkpatrick (Portland, OR), Donald D. Duncan (Silver Spring, MD)
Primary Examiner: Gregory J. Toatley, Jr.
Assistant Examiner: Michael A. Lyons
Attorney: Klarquist Sparkman, LLP
Application Number: 10/172,754
International Classification: G01B 11/02 (20060101);